Gurobi will solve the model as defined by the user. However, when evaluating a candidate solution for feasibility, in order to account for possible round-off errors in the floating-point evaluations, we must allow for some tolerances.
To be more precise, satisfying Optimality Conditions requires us to test at least the following three criteria:
- Integrality of solutions, i.e., whether a integer variable takes an integer value or not. More precisely, will be considered integral if abs(x - floor(x + 0.5)) IntFeasTol.
- Feasibility of primal constraints, i.e., whether holds for the primal solution. More precisely, will be considered to hold if (a * x) - b FeasibilityTol.
- Feasibility of dual constraints, i.e., whether holds for the dual solution. More precisely, will be considered to hold if (a * y) - c OptimalityTol.
It is very important to note that the usage of these tolerances implicitly defines a gray zone in the search space in which solutions that are very slightly infeasible can still be accepted as feasible. However, the solver will not explicitly search for such solutions.
For this reason, it is actually possible (although highly unlikely for
well-posed problems) for a model to be reported as
being both feasible and infeasible (in the sense stated
above). This can occur if the model is infeasible in exact
arithmetic, but there exists a solution that is feasible within the
solver tolerances. For instance, consider: